As is well-known, a cellular communications system includes mobile radio receiver devices which can communicate with each other via base stations in the system. The system is set up as a cellular network, with each base station serving one or more cells depending on the cell structure. The mobile radio receiver devices include analog radio frequency (RF)/intermediate frequency (IF) stages which are arranged to receive and transmit wireless signals carrying data via one or more antennas. The output of the RF/IF stages is typically converted to baseband, where an analog to digital converter (ADC) converts incoming analogue signals to digital samples, which are then processed for signal detection and decoding of the data, e.g. in the form of logical values. The analog to digital converter may alternatively operate directly at IF, in which case the conversion to baseband is performed in the digital domain. A number of different types of front end processing of the digital samples are known to implement signal detection, including rake receiver processing and channel equalisation processing.
In code division multiple access wireless systems, different physical channels are multiplexed in the code domain using separate spreading sequences. In the case of orthogonal spreading code words, the original data symbols can then be effectively separated at the receiver by despreading. In a wideband CDMA (WCDMA) cellular system, downlink code multiplexing is performed using orthogonal variable spreading factor (OVSF) codes. However, the OVSF code words are orthogonal to each other only under the condition of perfect time alignment. In the presence of multi-path propagation, the code orthogonality is lost and the operation of despreading is effected by multiple access interference (MAI).
Conventional CDMA receivers based on rake processing (as described for example in J. G. Proakis, “Digital Communications”, published by McGraw & Hill, 1995) are subject to performance degradation due to loss of orthogonality between channelisation codes in the presence of multi-path propagation. For synchronous CDMA transmission, as in the case of the forward link of the third generation partnership project (3GPP) WCDMA standard, an effective approach to solve this problem is to use a chip level channel equaliser (for example as described in the paper by A. Klein “Data Detection Algorithms Specially Designed for the Downlink of CDMA Mobile Radio Systems”, in Proceedings of IEEE Vehicular Technology Conference, vol. 1, Phoenix, Ariz., May 1997, pp. 203-207). The use of channel equalisation processing generally produces a significant performance advantage over conventional rake processing, but at the cost of an increased implementation complexity.
The performance advantage provided by chip level equalisation is especially important for high data rate transmission, as in the case of the 3GPP high speed downlink packet access (HSDPA) standard.
Chip level equalisers for HSDPA receivers are typically linear equalisers based on a transversal filter structure.
The computation of the equaliser coefficients for implementing equaliser processing can be based on the Minimization of the Mean-Square Error (MMSE) at the equaliser output. In principle, this can be achieved by block processing, as mentioned for example in A. Klein, “Data Detection Algorithms Specially Designed for the Downlink of CDMA Mobile Radio Systems”, in Proceedings of IEEE Vehicular Technology Conference, vol. 1, Phoenix, Ariz., May 1997, pp. 203-207, or by means of an adaptive algorithm, as mentioned in K. Hooli, M. Latva-aho and M. Juntti, “Performance Evaluation of Adaptive Chip-Level Channel Equalisers in WCDMA Downlink”, in Proceedings of IEEE International Conference on Communications, vol. 6, Helsinki, Finland, June 2001, pp. 1974-1979.
With respect to the calculation of the equaliser coefficients, the conventional MMSE criterion has the disadvantage of relying on specific assumptions on the statistics of the input disturbance (noise-plus-interference). An HSDPA MMSE equaliser typically assumes that the inter-cell interference can be modelled as an Additive White Gaussian Noise (AWGN) process. From this point of view, a more robust approach is to compute the equaliser coefficients based the Least-Squares (LS) criterion, where the calculation of the equaliser coefficients relies directly on the sample statistics of the input signal, without making any assumption on the statistics of the interference, as discussed, for example, in S. Haykin, Adaptive Filter Theory, Upper Saddle River, N.J.: Prentice Hall, 2001.
For both MMSE and LS equaliser, the processing relies on input signal samples collected from a suitable time interval, which should be selected in order to realize the best trade-off between the conflicting requirements of reducing the estimation errors by averaging over a wider time window, and of tracking the time variations of the propagation channel by averaging over a narrower time window for higher mobile speed.
International Application Publication No. WO 2009/0565505 discloses selecting a specific equaliser implementation based on different channel conditions, for example, on estimates of cell geometry and Doppler frequency. A particular method of performing cell geometry for this purpose is recited in International Application Publication No. WO 2009/056503.